Analysis of Contributions to NO
2
Ambient Air Quality Levels in
Madrid City (Spain) through Modeling. Implications for the
Development of Policies and Air Quality Monitoring
Rafael Borge
*, David de la Paz, Julio Lumbreras, Javier Pérez, Michel Vedrenne
Laboratory of Environmental Modelling, Technical University of Madrid (UPM), SpainEmail:
Received November 2013
As environmental standards become more stringent (e.g. European Directive 2008/50/EC), more reliable and sophisticated modeling tools are needed to simulate measures and plans that may effectively tackle air quality exceedances, common in large cities across Europe, particularly for NO2. Modeling air quality in
urban areas is rather complex since observed concentration values are a consequence of the interaction of multiple sources and processes that involve a wide range of spatial and temporal scales. Besides a consis-tent and robust multi-scale modeling system, comprehensive and flexible emission inventories are needed. This paper discusses the application of the WRF-SMOKE-CMAQ system to the Madrid city (Spain) to assess the contribution of the main emitting sectors in the region. A detailed emission inventory was compiled for this purpose. This inventory relies on bottom-up methods for the most important sources. It is coupled with the regional traffic model and it makes use of an extensive database of industrial, com-mercial and residential combustion plants. Less relevant sources are downscaled from national or regional inventories. This paper reports the methodology and main results of the source apportionment study per-formed to understand the origin of pollution (main sectors and geographical areas) and define clear targets for the abatement strategy. Finally the structure of the air quality monitoring is analyzed and discussed to identify options to improve the monitoring strategy not only in the Madrid city but the whole metropolitan area.
Keywords: Air Quality Modeling; Source Apportionment; NO2; CMAQ; Urban Air Quality; Madrid
Introduction
Despite recent efforts made in Europe, a large percentage of the European population is currently exposed to air pollutant concentrations above the European Union and World Health Organization reference levels (EEA, 2013). This is mainly rele- vant to urban areas, where the majority of the European popula- tion lives, leading to adverse effects on public health. Even from the regulatory point of view, there are still pending issues in Europe. For instance, air concentrations of NO2 lags clearly
behind the decreasing trend of NOX emissions (Guerreiro, C. et
al., 2010). As a consequence, the main cities in Europe are finding serious difficulties to comply with the air quality stan- dards defined in the EU Directive 2008/50/EC for NO2. Non-
attendant areas and urban agglomerations are legally bound to develop and apply air quality plans (AQPs) to meet legal stan- dards. Such plans and strategies are based on abatement meas- ures that usually entail significant social, economic and politi- cal costs. Therefore, it is important to guarantee that any meas- ure is actually aimed at the sectors causing air quality problems so they can effectively improve air quality levels.
The inherent complexity of urban environments requires si- mulation tools to assess air quality levels to be able to support the analysis and evaluation of a variety of policies and emission abatement measures previous to their implementation (Denby, B. et al., 2011).
This evaluation must begin with a source apportionment analysis that can provide essential information regarding the basic emission abatement strategy, the maximum feasible air quality improvement related to the main emitting sectors as well as the external constrains. This paper reports on the source apportionment carried out for the Madrid city (Spain) for the development of an AQP to comply with NO2 standards in the
near future (Borge, R. et al., 2014). Despite satisfying a legal requirement, this analysis is also useful to assess the layout of the air quality monitoring stations and to identify options to improve the monitoring strategy in the region.
Case Study
Madrid is the capital and largest city in Spain, located in the centre of the Iberian Peninsula with a total population of 5 mil- lion people in its metropolitan area. Madrid City Council and surrounding municipalities are connected through a dense road network and constitute a continuous urban area with a large density of air quality monitoring stations (Figure 1).
Despite the experienced population and traffic increase, air quality levels have improved in the city over the last decade. However, some pollutants like nitrogen dioxide (NO2) still
exceed the limit values (LV) according to the European legisla-tion. The NO2 annual average recorded in most of the city’s
traffic air quality monitoring stations is above the LV (40 µg/m3), as it can be seen in the records of the monitoring station
Figure 1.
Madrid city metropolitan area and air quality monitoring stations. Air quality monitoring stations of the Madrid City council and Madrid Greater Area networks are represented by circles and squares respec-tively. Traffic stations are represented in red, urban background in blue and industrial stations in green.
(Figure 2). This phenomenon is basically attributed to heavy traffic levels and to a strong dieselization of the fleet in recent years (Kassomenos, P. et al., 2006).
Methodology
Modeling System and Domains
The mesoscale modeling system is based on the Weather Re- search and Forecasting (WRF) (Skamarock & Klemp, 2008), the Sparse Matrix Operator Kernel Emissions (SMOKE) mod- eling system (Institute for the Environment, 2009), and the Community Multiscale Air Quality (CMAQ) (Byun and Ching, 1999; Byun and Schere, 2006). Details about specific configu- ration and adaptation to the Spanish conditions can be found respectively in Borge et al. (2008a, 2008b, 2010).
Four nested domains were used in order to capture interna- tional, national, regional and local contributions to NO2 to am-
bient concentration in Madrid with a maximum resolution of 1 km2 (Table 1).
Special care was taken to keep the consistency among the emission inventories used across the scales. This is a crucial issue for the source apportionment exercise since it includes an analysis of emission contributions from different geographic areas. Further information about emission compilation and harmonization can be found in Borge, R. et al. (2014). The emission inventory for the innermost domain (Figure 1) is based on a combination of top-down and bottom-up methods specific for each of the main emission sources in the city. The most relevant feature is the integration of a the City Concil’s mesoscale road traffic model within the emission model so very detailed, 1-h, 1-km2 resolved emissions can be computed for
Table 1.
Domains for the mesoscale modelling system.
Domain Geographic scope X-Y dimensions (km)
Horizontal resolution (km)
D1 Europe 6144 × 5376 48
D2 Iberian Peninsula 1200 × 960 16
D3 Greater Madrid Region 192 × 192 4
D4 Madrid Metropolitan Area 40 × 44 1
0 50 100 150 200 250 300 350 400 450
0 20 40 60 80 100 120 140
Annual m ean NO2 concentration (µg/m 3
)
1
9
th
h
ig
h
e
s
t h
o
u
rl
y
N
O
2
c
o
n
c
e
n
tr
a
ti
o
n
(
µ
g
/m
3)
Traffic stations
Urban background stations
Suburban background stations
Figure 2.
Observed NO2 values (corresponding to the annual and hourly NO2
limit values defined in the European AQ Directive) in the Madrid air quality monitoring network for the years 2010-2012.
any given traffic situation (Borge, R. et al., 2012a). Total emis-sions at SNAP group level in the innermost modeling domain (Figure 1) are given in Table 2.
Source Apportionment Methodology
A zero-out methodology was followed in this application. The contribution of a particular emission source or region can be estimated through the brute force method (BFM), sometimes referred to as single-perturbation method (Samaali, M. et al., 2011 and references within). This method relies on the analysis of the change in the pollutant concentration that would occur if a given emitting source is removed from the simulation (usually referred to as zero-out sensitivity runs). This approach has been used in the past to isolate the response of complex, nonlinear systems to one particular sector in source apportionment and sensitivity analysis (Cohan, D.S. et al., 2005). This method has inherent limitations in accurately describing sensitivities but it may be useful to approximate the effect of potential emission reductions in a particular source or origin area as pointed out before in several studies (Koo, B. et al., 2009; Carmichael, G. et al., 2010; Leung et al, 2007). This analysis was originally performed separately for those locations inside and outside the Madrid municipality, since this is a legal requirement. For this study, both analyses have been merged so a more scientifically- sound and coherent view can be given. Reductions of 100% (zero-out) were simulated for the most relevant anthropogenic emissions:
Table 2.
Emissions in the Madrid metropolitan area (metric tons per year).
SNAP
group* CO NH3 NOX PM10 PM2.5 SO2 VOC
01 225 0 243 50 29 1128 1
02 10004 0 3680 520 410 2731 1104
03 2238 0 10689 265 210 2494 1217
04 1083 130 108 51 32 70 3782
05 0 15 0 0 0 0 2056
06 0 212 0 0 0 0 48828
07 22070 250 27961 1506 1205 157 4365
08 2711 0 4171 360 360 287 769
09 441 2036 1769 26 26 6 5267
10 357 1543 56 90 13 0 17
11 32 605 125 0 0 0 4682
latoT 39161 4791 48802 2868 2285 6873 72088
*SNAP groups: 01—Combustion in energy and transformation industries;
02—Non-industrial combustion plants; 03—Combustion in manufacturing industry; 04—Production processes; 05—Extraction and distribution of fossil fuels; 06—Solvent and other product use; 07—Road transport; 08—Other mobile sources and machinery; 09—Waste treatment and disposal; 10—Agri- culture; 11—Other sources and sinks (Nature).
(SNAP 02);
• Industry (SNAP 03 & 04); • Road traffic (SNAP 07);
• Other mobile sources (planes, trains, machinery) (SNAP 08);
• Sources outside the modeling domain (rest of the country and international contributions).
The total impact and therefore, the maximum theoretical benefits that can be harvested by implementing abatement op- tions in these sectors were derived from the comparison of the assessment of the individual runs with the base case (consider- ing all emissions). All the emission processing and scenarios for the sensitivity runs were done through the SMOKE system. Further details on this procedure can be found in R. Borge et al., (2012b).
Results and Discussion
Individual results for the model grid cells where air quality monitoring traffic stations are located are shown in Figure 3. As expected, according to their classification, NO2 concentra-
tion levels at all these locations are clearly dominated by con- tributions form road traffic, with shares ranging form 59.8% to 75.3% and an average value of 69.4%. It is worth noting, how- ever that this contribution seems to be smaller for the stations located at the right-hand side of the graph. Those stations (COSL, ALCO, LEGA, GETA) correspond to monitoring sta- tions of the Greater Madrid Region, that they are located in the outskirts of the urban area. In contrast, the influence of sources outside the Madrid Region (rest of the country) is larger, which is totally reasonable. While national contributions inside the Madrid municipality are around 10% of total NO2 concentration
levels, they reach a 15% as an average in the monitoring sta- tions closer to the modeling boundary. A similar trend is seen for international contributions, although the differences are smaller (3.4% Vs 5.6%). In all cases local emissions (those originated inside the region) are responsible for the majority of the observed concentrations (around 85% as an average). The second source in importance is the RCI sector, responsible for 4.5% of NO2 ambient concentration. This ratio exhibits a larger
variability, with larger values in the city center (RCAJ, CUCA) except for some locations where other sectors (mainly SNAP 09) have a bigger influence locally due to the proximity to waste management plants. The contributions from other mobile sources are similar in all the traffic stations (around 4.2%). This is probably due to the difficulty to allocate emissions from gar- dening and cleaning machinery as well as construction and industrial vehicles, that are spatially distributed all over the city depending on the population and road network density. Finally, the results point out that the influence of industry in the air quality of the city center is negligible; (less than 2%). This makes sense, since the industrial activity in Madrid (very mod- erate) is located in the surroundings of the city. This can be clearly spotted in the results corresponding to the stations of the Greater Madrid Region, mainly Coslada (COSL) where 9.4% of NO2 is produced by industry. It should be noted that the SNAP
01 sector is totally unimportant in Madrid since there are no power plants in the region.
Results form non-traffic stations are shown in Figure 4. De- spite most of these stations are considered urban background locations, the structure of NO2 contributions is rather similar to
that shown in Figure 3. Contributions of road traffic emissions to air quality background levels roughly represent 2/3 of total pollution. As expected, industry has a bigger influence in these environments not directly exposed to traffic emissions. This is particularly true for those stations of the Greater Madrid Region (again those in the right-hand side in Figure 4) where industrial activity clearly constitutes the second most important pollution source (16.3% as an average). Although Alcobendas (ALCB) is the only station label as industrial, it does not present a special- ly high contribution from this sector (14.7%), lower that other stations such as Móstoles (MOST) and Torrejón (TOAZ) (15.1% and 21.2% respectively). The contribution of other sectors not explicitly considered in the analysis represents around 5.5% of total NO2 values as an average, although it
presents quite some variability. Barajas Pueblo presents an unusually high share for this sector (16.0%). This may be due to the influence of emissions generated in the Madrid/Barajas international airport neither included in the SNAP 08 nor the SNAP 07 sector.
Conclusion
A comprehensive source apportionment study was performed in the Madrid metropolitan area through the application of a multi-scale, multi-pollutant air quality modeling system (WRF- SMOKE-CMAQ). The study was based in the single-perturba- tion method, i.e. removing emissions from each of the main sectors one at a time and comparing the results with those of the baseline scenario (complete emission inventory).
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
PESP EAGU RCAJ MORZ CUCA BAPI PFEL PCLL CSLL COSL ALCO LEGA GETA
Other sectors
RCI Industry
Other mobile sources
Road traffic National International
Figure 3.
Results of the NO2 source apportionment analysis performed at the location of traffic air quality
monitor-ing stations.
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
ARSO FRLL BJPU PCAM VALL MALV ECNV TROL URBA TOAZ MOST RIVA ALCB
Other sectors
RCI
Industry
Other mobile sources
Road traffic
National
International
Figure 4.
Results of the NO2 source apportionment analysis performed at the location of (mostly) urban background
monitoring stations.
NO2 levels all over the region, specially in the city center with
contributions up to 75% of total concentration (86% if non- local sources, those outside the Greater Madrid Region are not considered). Contributions form other countries are relatively unimportant (around 1% in most cases). Similarly, pollution from outside the region has a small incidence (approximately 3% - 4%). National influences (sources in Spain but outside the Greater Madrid Region) are more important, mainly in the city outskirts, but it represents less than 10% of total NO2 as an
average) This means that NO2 levels observed in Madrid are
basically due to local sources, so air quality issues can be tackled by local AQP. In particular, these plans should be clear- ly directed to reduce emissions in the road traffic sector, re- sponsible of 69.4% and 66.4% of nitrogen dioxide ambient concentration observed in traffic and urban background loca- tions respectively (as an average). These figures indicate that abatement emissions in the road traffic sector may be particu- larly effective. As shown in Table 2, the relative contribution of the SNAP 07 sector is 57.3% in the geographical domain analyzed (D4).
While their contribution in terms of emissions is well below 60%, their share in resulting NO2 ambient concentration levels
is close to 70%. This may be related to two factors: 1) emis- sions form road traffic are released at ground level (unlike those of the RCI or industrial sectors) and 2) their NO2/NOX emission
factor is higher due to a strong fleet dieselization phenomenon (R. Borge et al., 2012).
This conclusion is clearly reflected in the Madrid AQP for the horizon 2011-2015 (Madrid City Council, 2012). Consis- tently with the results of the source apportionment exercise performed, most of the measures in the plan (up to 70) were targeted to the road traffic sector. This abatement strategy in- cludes a Low Emission Zone (LEZ), reduction of road capacity and pedestrianized areas in the city center, renovation of city bus fleet to incorporate clean technologies (electric, hybrid natural gas-fuelled buses), etc. According to the results shown in this AQP would achieve a 40% reduction of NOX emissions
from the road traffic sector in the modeling domain. As a result of all these measures, a global decrease of 31% in NOX emis-
sions is expected in the year 2014 within D4 (respective to the emissions of 2007 used as reference scenario for the source apportionment analysis presented here). Additional model runs (R. Borge et al., (2014) indicate that annual NO2 levels may be
reduced by 34% as an average; approximately 15 μg/m3
in the city centre, also with an important impact in the metropolitan area (−7 μg/m3 as an average in the modeling domain). 1-hour concentration peaks may also decline by 40% approximately in most of the city (Figure 5).
Figure 5.
Expected effect of the Madrid AQP in NO2 concentration. Annual average for year 2007, considered for
the source apportionment study (a) and 2014, temporal horizon of the AQP (b).
served NO2 in Madrid. A similar influence can be attributed to
mobile sources other than road traffic. The contribution of in- dustry is low in general although it may be important in the surroundings of the city. According to the source apportion- ment study performed, the industrial sources in the region are responsible of 5% and 16% of NO2 levels in the traffic and
urban background stations of the Greater Madrid Region, and therefore may constitute a sensible target for additional meas- ures, especially in the east area of the Madrid metropolitan area.
As for the air quality monitoring strategy, the results form this study indicate that the relative amount of traffic stations, although high, may be adequate since traffic is mainly respon- sible for air quality problems in the region. However, further analysis should be done to understand to what extend this sta- tions may be providing redundant results (the source appor- tionment results are very similar in all the stations) and there- fore the air quality monitoring network may be simplified and reduced, considering the minimum requirements established in the Directive 2008/50/EC. On the other hand, the potential need for some industrial stations in the eastern part of the Madrid metropolitan area may be considered.
Acknowledgements
The Madrid city Council provided the traffic model and supported this study. The CMAQ modeling system was made available by the US EPA and it is supported by the Community Modeling and Analysis System (CMAS) Center. The authors also acknowledge the use of emission datasets and monitoring data from the Spanish and Portuguese Ministries of Environ- ment as well as air quality monitoring data from the Madrid city Council and Greater Madrid Region.
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